ACS Detention Admissions Exploration¶

In [1]:
#install packages
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import geopandas as gpd
import shapely
In [2]:
#read file 
ACS_detention_by_CD = pd.read_csv('/Users/aren_kab/Documents/ACS Job Prep/Detention_Admissions_by_Community_District_20240923.csv')
In [3]:
#Get info 
ACS_detention_by_CD.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 325 entries, 0 to 324
Data columns (total 4 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   Calendar Year    325 non-null    int64 
 1   Borough          325 non-null    object
 2   Borough/CD       325 non-null    object
 3   Number Admitted  325 non-null    int64 
dtypes: int64(2), object(2)
memory usage: 10.3+ KB
In [4]:
#head of data set - look at data 
ACS_detention_by_CD.head(5)
Out[4]:
Calendar Year Borough Borough/CD Number Admitted
0 2019 BRONX BX01 Mott Haven/Melrose 62
1 2019 BRONX BX02 Hunts Point/Longwood 22
2 2019 BRONX BX03 Morrisania/Crotona 49
3 2019 BRONX BX04 Highbridge/Concourse 45
4 2019 BRONX BX05 Fordham/University Heights 42
In [5]:
#summerize table
ACS_detention_by_CD.describe()
Out[5]:
Calendar Year Number Admitted
count 325.000000 325.000000
mean 2021.000000 22.781538
std 1.416394 20.441990
min 2019.000000 0.000000
25% 2020.000000 8.000000
50% 2021.000000 16.000000
75% 2022.000000 33.000000
max 2023.000000 108.000000
In [6]:
ACS_detention_by_CD.head()
Out[6]:
Calendar Year Borough Borough/CD Number Admitted
0 2019 BRONX BX01 Mott Haven/Melrose 62
1 2019 BRONX BX02 Hunts Point/Longwood 22
2 2019 BRONX BX03 Morrisania/Crotona 49
3 2019 BRONX BX04 Highbridge/Concourse 45
4 2019 BRONX BX05 Fordham/University Heights 42

Get Started With This Data:¶

What boroughs are young people entering detention most often from?¶

Extra: What is the distribution of detention admissions accross boroughs for 2020 (incuding population)?¶

What community districts are young people entering detention most often from?¶

What is the ranking of home community districts for youth entering detention?¶

In [7]:
### What boroughs are young people entering detention most often from?
### Extra: What is the distribution of detention admissions accross All boroughs look like?
## DO sum by boroughs 
ACS_Borough_Sums_all_years = ACS_detention_by_CD.groupby('Borough')[['Number Admitted']].sum().reset_index().sort_values(by='Number Admitted',ascending=False)
ACS_Borough_Sums_all_years
Out[7]:
Borough Number Admitted
0 BRONX 2136
1 BROOKLYN 2081
4 QUEENS 1331
2 MANHATTAN 1138
5 STATEN ISLAND 388
3 OUTSIDE OF NYC/UNKNOWN 330
In [8]:
#Lets restrict to only Boroughs we know 
ACS_Borough_Sums_all_years = ACS_Borough_Sums_all_years.loc[:5]
#Fix Case
ACS_Borough_Sums_all_years['Borough']= ACS_Borough_Sums_all_years['Borough'].str.title()

/var/folders/vp/2flcfxx12ns473tz77qt51v80000gn/T/ipykernel_6672/2180205020.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  ACS_Borough_Sums_all_years['Borough']= ACS_Borough_Sums_all_years['Borough'].str.title()
In [9]:
ACS_Borough_Sums_all_years = ACS_Borough_Sums_all_years.sort_values(by='Borough',ascending=True)
ACS_Borough_Sums_all_years
Out[9]:
Borough Number Admitted
0 Bronx 2136
1 Brooklyn 2081
2 Manhattan 1138
4 Queens 1331
5 Staten Island 388
In [10]:
import plotly.express as px

fig1 = px.bar(ACS_Borough_Sums_all_years, x="Borough", y="Number Admitted",color="Borough",text="Number Admitted", title="Juvinelle Detention by Borough (All Years)")
fig1.update_layout(showlegend=False)
##If you want to export to html - fig.write_html("path/to/file.html")
In [11]:
#Lets isolate these results to one year - and do it in context of population
ACS_Borough_2020 = ACS_detention_by_CD[ACS_detention_by_CD['Calendar Year'] == 2020]
ACS_Borough_2020_Sums = ACS_Borough_2020.groupby('Borough')[['Number Admitted']].sum().reset_index().sort_values(by='Number Admitted', ascending=False)
ACS_Borough_2020_Sums
Out[11]:
Borough Number Admitted
0 BRONX 321
1 BROOKLYN 314
4 QUEENS 202
2 MANHATTAN 168
5 STATEN ISLAND 47
3 OUTSIDE OF NYC/UNKNOWN 33
In [12]:
#Lets isolate these results to one year - and do it in context of population
#first_read in pop dataset
nyc_pop_boro = pd.read_csv('/Users/aren_kab/Documents/ACS Job Prep/New_York_City_Population_by_Borough__1950_-_2040_20240923.csv')
nyc_pop_boro.head(6)
Out[12]:
Age Group Borough 1950 1950 - Boro share of NYC total 1960 1960 - Boro share of NYC total 1970 1970 - Boro share of NYC total 1980 1980 - Boro share of NYC total ... 2000 2000 - Boro share of NYC total 2010 2010 - Boro share of NYC total 2020 2020 - Boro share of NYC total 2030 2030 - Boro share of NYC total 2040 2040 - Boro share of NYC total
0 Total Population NYC Total 7891957 100.00 7781984 100.00 7894862 100.00 7071639 100.00 ... 8008278 100.00 8242624 100.00 8550971 100.00 8821027 100.00 9025145 100.00
1 Total Population Bronx 1451277 18.39 1424815 18.31 1471701 18.64 1168972 16.53 ... 1332650 16.64 1385108 16.80 1446788 16.92 1518998 17.22 1579245 17.50
2 Total Population Brooklyn 2738175 34.70 2627319 33.76 2602012 32.96 2230936 31.55 ... 2465326 30.78 2552911 30.97 2648452 30.97 2754009 31.22 2840525 31.47
3 Total Population Manhattan 1960101 24.84 1698281 21.82 1539233 19.50 1428285 20.20 ... 1537195 19.20 1585873 19.24 1638281 19.16 1676720 19.01 1691617 18.74
4 Total Population Queens 1550849 19.65 1809578 23.25 1986473 25.16 1891325 26.75 ... 2229379 27.84 2250002 27.30 2330295 27.25 2373551 26.91 2412649 26.73
5 Total Population Staten Island 191555 2.43 221991 2.85 295443 3.74 352121 4.98 ... 443728 5.54 468730 5.69 487155 5.70 497749 5.64 501109 5.55

6 rows × 22 columns

In [13]:
import string
nyc_pop_boro.Borough= nyc_pop_boro['Borough'].astype('string').str.title()
#capitilize for standardization
In [14]:
#only get 2020 pop
nyc_pop_boro_2020 = nyc_pop_boro[["Borough","2020"]].copy()
nyc_pop_boro_2020.Borough= nyc_pop_boro_2020['Borough'].astype('string')
In [15]:
# only keep NYC boroughs + index
nyc_pop_boro_2020 = nyc_pop_boro_2020.loc[1:].set_index(['Borough'])
nyc_pop_boro_2020
Out[15]:
2020
Borough
Bronx 1446788
Brooklyn 2648452
Manhattan 1638281
Queens 2330295
Staten Island 487155
In [16]:
ACS_Borough_2020_Sums['Borough'] = ACS_Borough_2020_Sums['Borough'].astype('string')
#capatilize for merging
ACS_Borough_2020_Sums['Borough'] = ACS_Borough_2020_Sums['Borough'].str.title()
ACS_Borough_2020_Sums
Out[16]:
Borough Number Admitted
0 Bronx 321
1 Brooklyn 314
4 Queens 202
2 Manhattan 168
5 Staten Island 47
3 Outside Of Nyc/Unknown 33
In [17]:
#only keep NYC boroughs + index
ACS_Borough_2020_Sums=ACS_Borough_2020_Sums.loc[:5].set_index(['Borough'])
ACS_Borough_2020_Sums
Out[17]:
Number Admitted
Borough
Bronx 321
Brooklyn 314
Queens 202
Manhattan 168
Staten Island 47
In [18]:
nyc_pop_boro_2020
Out[18]:
2020
Borough
Bronx 1446788
Brooklyn 2648452
Manhattan 1638281
Queens 2330295
Staten Island 487155
In [19]:
ACS_Borough_2020_Sums
Out[19]:
Number Admitted
Borough
Bronx 321
Brooklyn 314
Queens 202
Manhattan 168
Staten Island 47
In [20]:
ACS_Borough_2020_Sums.index
Out[20]:
Index(['Bronx', 'Brooklyn', 'Queens', 'Manhattan', 'Staten Island'], dtype='string', name='Borough')
In [21]:
nyc_pop_boro_2020.index
Out[21]:
Index(['   Bronx', '   Brooklyn', '   Manhattan', '   Queens',
       '   Staten Island'],
      dtype='string', name='Borough')
In [22]:
#index is off here with spacing_fix it
nyc_pop_boro_2020.index = nyc_pop_boro_2020.index.str.strip()
In [23]:
Merged_ACS_Pop_Data_2020 = ACS_Borough_2020_Sums.merge(nyc_pop_boro_2020, on='Borough', how='right')
Merged_ACS_Pop_Data_2020
Out[23]:
Number Admitted 2020
Borough
Bronx 321 1446788
Brooklyn 314 2648452
Manhattan 168 1638281
Queens 202 2330295
Staten Island 47 487155
In [24]:
#Population Share - do Admitted / population -  its % BUT made more leg|
Merged_ACS_Pop_Data_2020['Juvenille_Admitted_Pop_Density(%*10)'] = ((Merged_ACS_Pop_Data_2020['Number Admitted']/Merged_ACS_Pop_Data_2020['2020'])*10000)
In [25]:
Merged_ACS_Pop_Data_2020 = Merged_ACS_Pop_Data_2020
Merged_ACS_Pop_Data_2020= Merged_ACS_Pop_Data_2020.reset_index()
Merged_ACS_Pop_Data_2020
Out[25]:
Borough Number Admitted 2020 Juvenille_Admitted_Pop_Density(%*10)
0 Bronx 321 1446788 2.218708
1 Brooklyn 314 2648452 1.185598
2 Manhattan 168 1638281 1.025465
3 Queens 202 2330295 0.866843
4 Staten Island 47 487155 0.964785
In [26]:
# WE can see here not only does the bronx 
fig2 = px.bar(Merged_ACS_Pop_Data_2020, x=Merged_ACS_Pop_Data_2020['Borough'], y=Merged_ACS_Pop_Data_2020['Juvenille_Admitted_Pop_Density(%*10)'],color=Merged_ACS_Pop_Data_2020['Borough'],text="Number Admitted", title="Juvinelle Detention by Borough 2020 (w/ Population Density)")
fig2.update_layout(showlegend=False)
fig2.show(figsize=(20,9))

In Context this data is revealing: the Bronx has a double share of all other boroughs in terms of denity, previous results are decieving at first glance despite overall numbers due to population in each borough.¶

Important to keep in mind a data caveat here is that this is overall population not juvenille population-¶

In [27]:
#Grouped bar \
import plotly.graph_objects as go
fig = go.Figure(data=[
    go.Bar(name='Overall Detention', x=ACS_Borough_Sums_all_years["Borough"], y=ACS_Borough_Sums_all_years["Number Admitted"], yaxis='y',offsetgroup=1),
    go.Bar(name='Population Density Adjusted (%*10)', x=ACS_Borough_Sums_all_years["Borough"], y=Merged_ACS_Pop_Data_2020['Juvenille_Admitted_Pop_Density(%*10)'], yaxis='y2',offsetgroup=2)

# Change the bar mode
 ],
    layout={
        'yaxis': {'title': 'Overall Detention Axis'},
        'yaxis2': {'title': 'Population Density Axis', 'overlaying': 'y', 'side': 'right'}
    }
)

# Change the bar mode
fig.update_layout(barmode='group')
fig.show()

Now lets map this data¶

In [28]:
import geopandas as gpd

Borough_Boundry = gpd.read_file("/Users/aren_kab/Documents/ACS Job Prep/Borough Boundaries.geojson")
Borough_Boundry
Out[28]:
boro_code boro_name shape_area shape_leng geometry
0 3 Brooklyn 1934142775.63 728147.130565 MULTIPOLYGON (((-73.86327 40.58388, -73.86381 ...
1 5 Staten Island 1623618684.31 325910.306061 MULTIPOLYGON (((-74.05051 40.56642, -74.05047 ...
2 1 Manhattan 636646082.483 360037.525777 MULTIPOLYGON (((-74.01093 40.68449, -74.01193 ...
3 2 Bronx 1187174772.32 463180.578878 MULTIPOLYGON (((-73.89681 40.79581, -73.89694 ...
4 4 Queens 3041418003.6 888197.010944 MULTIPOLYGON (((-73.82645 40.59053, -73.82642 ...
In [29]:
Juvinille_data_including_mapping = Borough_Boundry.merge(Merged_ACS_Pop_Data_2020, left_on='boro_name', right_on='Borough',
          suffixes=('_left', '_right'))
Juvinille_data_including_mapping
Out[29]:
boro_code boro_name shape_area shape_leng geometry Borough Number Admitted 2020 Juvenille_Admitted_Pop_Density(%*10)
0 3 Brooklyn 1934142775.63 728147.130565 MULTIPOLYGON (((-73.86327 40.58388, -73.86381 ... Brooklyn 314 2648452 1.185598
1 5 Staten Island 1623618684.31 325910.306061 MULTIPOLYGON (((-74.05051 40.56642, -74.05047 ... Staten Island 47 487155 0.964785
2 1 Manhattan 636646082.483 360037.525777 MULTIPOLYGON (((-74.01093 40.68449, -74.01193 ... Manhattan 168 1638281 1.025465
3 2 Bronx 1187174772.32 463180.578878 MULTIPOLYGON (((-73.89681 40.79581, -73.89694 ... Bronx 321 1446788 2.218708
4 4 Queens 3041418003.6 888197.010944 MULTIPOLYGON (((-73.82645 40.59053, -73.82642 ... Queens 202 2330295 0.866843
In [30]:
Juvinille_data_including_mapping.plot(
    column="Number Admitted",
    cmap='copper', 
    legend=True,
    legend_kwds={"label": "Admissions in 2020", "orientation": "horizontal"},
);
In [31]:
import plotly.express as px
import geopandas as gpd
 

Juvinille_data_including_mapping.explore(
    column="Juvenille_Admitted_Pop_Density(%*10)",  # make choropleth based on "BoroName" column
    tooltip="boro_name",  # show "BoroName" value in tooltip (on hover)
    popup=True,  # show all values in popup (on click)
    tiles="CartoDB positron",  # use "CartoDB positron" tiles,
    legend_kwds=dict(colorbar=False),
    cmap="copper",  # use "Set1" matplotlib colormap
    style_kwds=dict(color="black"),  # use black outline
)
Out[31]:
Make this Notebook Trusted to load map: File -> Trust Notebook

2. What community districts are young people entering detention most often from?¶

3. What is the ranking of home community districts for youth entering detention?¶

In [32]:
#Bring Back the main Dataset 
ACS_detention_by_CD.head(5)
Out[32]:
Calendar Year Borough Borough/CD Number Admitted
0 2019 BRONX BX01 Mott Haven/Melrose 62
1 2019 BRONX BX02 Hunts Point/Longwood 22
2 2019 BRONX BX03 Morrisania/Crotona 49
3 2019 BRONX BX04 Highbridge/Concourse 45
4 2019 BRONX BX05 Fordham/University Heights 42
In [33]:
#Let's Group it by CD
ACS_CD_Sums = ACS_detention_by_CD.groupby('Borough/CD')[['Number Admitted']].sum().reset_index().sort_values(by='Number Admitted', ascending=False)
ACS_CD_Sums['Borough/CD'] = ACS_CD_Sums['Borough/CD'].astype('string')

ACS_CD_Sums.head(10)
Out[33]:
Borough/CD Number Admitted
4 BK05 East New York/Starrett City 339
42 Outside of NYC/Unknown 330
52 QN12 Jamaica/Hollis 302
60 Unknown CD 301
57 S101 Saint George/Stapleton 288
40 MN11 East Harlem 274
15 BK16 Brownsville 268
29 BX12 Williamsbridge/Baychester 265
26 BX09 Parkchester/Soundview 246
18 BX01 Mott Haven/Melrose 242
In [34]:
ACS_CD_Sums.dtypes
Out[34]:
Borough/CD         string
Number Admitted     int64
dtype: object
In [35]:
#Get rid of unknown to get true top 10
mask = ACS_CD_Sums['Borough/CD'] == ('Outside of NYC/Unknown')
ACS_CD_Sums = ACS_CD_Sums[~mask]
mask2 = ACS_CD_Sums['Borough/CD'] == ('Unknown CD')
ACS_CD_Sums = ACS_CD_Sums[~mask2]
ACS_CD_Sums.head(10)
Out[35]:
Borough/CD Number Admitted
4 BK05 East New York/Starrett City 339
52 QN12 Jamaica/Hollis 302
57 S101 Saint George/Stapleton 288
40 MN11 East Harlem 274
15 BK16 Brownsville 268
29 BX12 Williamsbridge/Baychester 265
26 BX09 Parkchester/Soundview 246
18 BX01 Mott Haven/Melrose 242
54 QN14 Rockaway/Broad Channel 233
22 BX05 Fordham/University Heights 233
In [36]:
#Plot top 5
ACS_CD_Sums_for_plot=ACS_CD_Sums.head(5)
In [37]:
fig3 = px.bar(ACS_CD_Sums_for_plot, y=ACS_CD_Sums_for_plot['Borough/CD'], x=ACS_CD_Sums_for_plot['Number Admitted'],color=ACS_CD_Sums_for_plot['Borough/CD'],text="Number Admitted", title="Top 5 CDs w/ Juvinille Admissions",orientation='h')
fig3.update_layout(showlegend=False)

fig3.show()

Intrestingly enough each of the boroughs are represented in the top 5 community Districts of admissions, remember that this is not with context of population of CD and just overall numbers admitted¶